Short-term forecasting up to twelve hours in advance allows for predicting weather conditions with higher spatial and temporal precision than longer time ranges. This makes it possible for these forecasts to have substantial impact on society by helping with daily planning, energy management, transportation and the mitigation of extreme weather events, among others [1]}. Short-term forecasting is also a longstanding scientific challenge that combines our best understanding of the physics of the atmosphere with our most advanced computational capabilities.
Current operational models for short-term forecasting are Numerical Weather Prediction (NWP) models that rely on physics-based simulations. The atmospheric simulations make use of supercomputers with heterogeneous hardware that run virtually continuously in data centers around the globe and update the forecasts based on the latest observations.
The weather conditions that the models predict include hundreds of atmospheric and oceanic features. The forecasts usually have a frequency of one or more hours and a grid resolution of 3 km to 12 km. The accuracy of a physics-based forecast is tied to the grid resolution as more precise physics simulations require a finer representation of the state of the atmosphere.
This relationship creates a computational bottleneck inherent to physics-based models that has proven challenging to overcome [2]}. Besides resolution, the accuracy of the forecasts also depends on how well the physical models used in NWP describe the atmosphere at the various relevant scales; improving these models is a substantial scientific challenge by itself [3]}.
